Overview

Dataset statistics

Number of variables44
Number of observations20000
Missing cells286735
Missing cells (%)32.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 MiB
Average record size in memory352.0 B

Variable types

Numeric40
Categorical4

Alerts

SBP is highly correlated with MAP and 1 other fieldsHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with SBP and 1 other fieldsHigh correlation
BaseExcess is highly correlated with HCO3 and 1 other fieldsHigh correlation
HCO3 is highly correlated with BaseExcessHigh correlation
pH is highly correlated with BaseExcessHigh correlation
BUN is highly correlated with CreatinineHigh correlation
Creatinine is highly correlated with BUNHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
ICULOS is highly correlated with HoursHigh correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Hours is highly correlated with ICULOSHigh correlation
SBP is highly correlated with MAP and 1 other fieldsHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with SBP and 1 other fieldsHigh correlation
BaseExcess is highly correlated with HCO3 and 1 other fieldsHigh correlation
HCO3 is highly correlated with BaseExcessHigh correlation
pH is highly correlated with BaseExcessHigh correlation
BUN is highly correlated with Creatinine and 1 other fieldsHigh correlation
Creatinine is highly correlated with BUN and 1 other fieldsHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Phosphate is highly correlated with BUN and 1 other fieldsHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
ICULOS is highly correlated with HoursHigh correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Hours is highly correlated with ICULOSHigh correlation
SBP is highly correlated with MAPHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with MAPHigh correlation
BaseExcess is highly correlated with HCO3High correlation
HCO3 is highly correlated with BaseExcessHigh correlation
BUN is highly correlated with CreatinineHigh correlation
Creatinine is highly correlated with BUNHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
ICULOS is highly correlated with HoursHigh correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Hours is highly correlated with ICULOSHigh correlation
Unit2 is highly correlated with Unit1High correlation
Unit1 is highly correlated with Unit2High correlation
EtCO2 has 16784 (83.9%) missing values Missing
BaseExcess has 19442 (97.2%) missing values Missing
HCO3 has 19584 (97.9%) missing values Missing
FiO2 has 14178 (70.9%) missing values Missing
pH has 14246 (71.2%) missing values Missing
PaCO2 has 14221 (71.1%) missing values Missing
SaO2 has 14875 (74.4%) missing values Missing
AST has 11536 (57.7%) missing values Missing
BUN has 1591 (8.0%) missing values Missing
Alkalinephos has 11530 (57.6%) missing values Missing
Calcium has 1550 (7.8%) missing values Missing
Chloride has 18383 (91.9%) missing values Missing
Creatinine has 1588 (7.9%) missing values Missing
Bilirubin_direct has 18529 (92.6%) missing values Missing
Glucose has 1173 (5.9%) missing values Missing
Lactate has 15240 (76.2%) missing values Missing
Magnesium has 3543 (17.7%) missing values Missing
Phosphate has 8365 (41.8%) missing values Missing
Potassium has 1434 (7.2%) missing values Missing
Bilirubin_total has 11522 (57.6%) missing values Missing
TroponinI has 13436 (67.2%) missing values Missing
Hct has 1953 (9.8%) missing values Missing
Hgb has 1941 (9.7%) missing values Missing
PTT has 15602 (78.0%) missing values Missing
WBC has 2000 (10.0%) missing values Missing
Fibrinogen has 18052 (90.3%) missing values Missing
Platelets has 1992 (10.0%) missing values Missing
Unit1 has 6095 (30.5%) missing values Missing
Unit2 has 6095 (30.5%) missing values Missing
FiO2 is highly skewed (γ1 = 76.23783285) Skewed
PatientID is uniformly distributed Uniform
PatientID has unique values Unique
HospAdmTime has 1145 (5.7%) zeros Zeros
SepsisLabel has 18858 (94.3%) zeros Zeros

Reproduction

Analysis started2021-11-29 10:23:20.454859
Analysis finished2021-11-29 10:23:33.739015
Duration13.28 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

PatientID
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct20000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110000.5
Minimum100001
Maximum120000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:33.788305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum100001
5-th percentile101000.95
Q1105000.75
median110000.5
Q3115000.25
95-th percentile119000.05
Maximum120000
Range19999
Interquartile range (IQR)9999.5

Descriptive statistics

Standard deviation5773.647028
Coefficient of variation (CV)0.05248746167
Kurtosis-1.2
Mean110000.5
Median Absolute Deviation (MAD)5000
Skewness0
Sum2200010000
Variance33335000
MonotonicityStrictly increasing
2021-11-29T11:23:33.896473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000011
 
< 0.1%
1133311
 
< 0.1%
1133381
 
< 0.1%
1133371
 
< 0.1%
1133361
 
< 0.1%
1133351
 
< 0.1%
1133341
 
< 0.1%
1133331
 
< 0.1%
1133321
 
< 0.1%
1133301
 
< 0.1%
Other values (19990)19990
> 99.9%
ValueCountFrequency (%)
1000011
< 0.1%
1000021
< 0.1%
1000031
< 0.1%
1000041
< 0.1%
1000051
< 0.1%
1000061
< 0.1%
1000071
< 0.1%
1000081
< 0.1%
1000091
< 0.1%
1000101
< 0.1%
ValueCountFrequency (%)
1200001
< 0.1%
1199991
< 0.1%
1199981
< 0.1%
1199971
< 0.1%
1199961
< 0.1%
1199951
< 0.1%
1199941
< 0.1%
1199931
< 0.1%
1199921
< 0.1%
1199911
< 0.1%

HR
Real number (ℝ≥0)

Distinct15131
Distinct (%)75.7%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean83.04871048
Minimum33.36
Maximum174.6976744
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:34.010773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum33.36
5-th percentile60.42152123
Q172.19230769
median82.17248284
Q392.6
95-th percentile109.1428571
Maximum174.6976744
Range141.3376744
Interquartile range (IQR)20.40769231

Descriptive statistics

Standard deviation14.99990916
Coefficient of variation (CV)0.1806157985
Kurtosis0.2303158873
Mean83.04871048
Median Absolute Deviation (MAD)10.17248284
Skewness0.3821272666
Sum1660642.015
Variance224.9972747
MonotonicityNot monotonic
2021-11-29T11:23:34.109911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6828
 
0.1%
72.523
 
0.1%
8023
 
0.1%
8621
 
0.1%
9021
 
0.1%
7020
 
0.1%
7720
 
0.1%
8319
 
0.1%
7619
 
0.1%
8119
 
0.1%
Other values (15121)19783
98.9%
ValueCountFrequency (%)
33.361
< 0.1%
37.285714291
< 0.1%
37.960526321
< 0.1%
38.783783781
< 0.1%
38.9843751
< 0.1%
39.867647061
< 0.1%
40.03260871
< 0.1%
40.931818181
< 0.1%
40.93751
< 0.1%
41.28260871
< 0.1%
ValueCountFrequency (%)
174.69767441
< 0.1%
158.21428571
< 0.1%
156.14285711
< 0.1%
1531
< 0.1%
148.51960781
< 0.1%
145.65454551
< 0.1%
145.21
< 0.1%
144.84090911
< 0.1%
143.23333331
< 0.1%
140.73404261
< 0.1%

O2Sat
Real number (ℝ≥0)

Distinct7737
Distinct (%)38.7%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean97.10576372
Minimum65.5
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:34.215945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum65.5
5-th percentile94
Q196.01860587
median97.32240143
Q398.47660576
95-th percentile99.65625
Maximum100
Range34.5
Interquartile range (IQR)2.457999889

Descriptive statistics

Standard deviation1.944590634
Coefficient of variation (CV)0.02002549137
Kurtosis22.29509362
Mean97.10576372
Median Absolute Deviation (MAD)1.221587214
Skewness-2.439035024
Sum1941532.64
Variance3.781432733
MonotonicityNot monotonic
2021-11-29T11:23:34.313928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100183
 
0.9%
98141
 
0.7%
97131
 
0.7%
97.5118
 
0.6%
98.5113
 
0.6%
99102
 
0.5%
96.592
 
0.5%
9691
 
0.5%
99.568
 
0.3%
98.2563
 
0.3%
Other values (7727)18892
94.5%
ValueCountFrequency (%)
65.51
< 0.1%
66.857142861
< 0.1%
67.725806451
< 0.1%
721
< 0.1%
72.583333331
< 0.1%
73.442857141
< 0.1%
74.333333331
< 0.1%
74.803921571
< 0.1%
75.588235291
< 0.1%
76.321428571
< 0.1%
ValueCountFrequency (%)
100183
0.9%
99.990740741
 
< 0.1%
99.98936171
 
< 0.1%
99.986111111
 
< 0.1%
99.985714291
 
< 0.1%
99.985294121
 
< 0.1%
99.9843751
 
< 0.1%
99.981818181
 
< 0.1%
99.980769231
 
< 0.1%
99.979166671
 
< 0.1%

Temp
Real number (ℝ≥0)

Distinct5252
Distinct (%)26.3%
Missing49
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean36.77130518
Minimum31.44444444
Maximum39.7375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:34.420733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum31.44444444
5-th percentile36
Q136.44
median36.72567568
Q337.0822479
95-th percentile37.67330317
Maximum39.7375
Range8.293055556
Interquartile range (IQR)0.6422478992

Descriptive statistics

Standard deviation0.5290535673
Coefficient of variation (CV)0.01438767443
Kurtosis3.225961007
Mean36.77130518
Median Absolute Deviation (MAD)0.3187687688
Skewness-0.009018216435
Sum733624.3097
Variance0.2798976771
MonotonicityNot monotonic
2021-11-29T11:23:34.516538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.5243
 
1.2%
36.6233
 
1.2%
36.4191
 
1.0%
36.7164
 
0.8%
36.9150
 
0.8%
36.8149
 
0.7%
36.3130
 
0.7%
37123
 
0.6%
36116
 
0.6%
36.55113
 
0.6%
Other values (5242)18339
91.7%
ValueCountFrequency (%)
31.444444441
< 0.1%
32.307142861
< 0.1%
32.442857141
< 0.1%
32.81
< 0.1%
32.932352941
< 0.1%
32.951
< 0.1%
33.083333331
< 0.1%
33.105769231
< 0.1%
33.351
< 0.1%
33.398611111
< 0.1%
ValueCountFrequency (%)
39.73751
< 0.1%
39.714516131
< 0.1%
39.6751
< 0.1%
39.507142861
< 0.1%
39.366666671
< 0.1%
39.222916671
< 0.1%
39.160294121
< 0.1%
39.11
< 0.1%
39.051
< 0.1%
38.981
< 0.1%

SBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15753
Distinct (%)78.9%
Missing24
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean126.4104727
Minimum54.54166667
Maximum203.775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:34.616674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum54.54166667
5-th percentile98.52083333
Q1112.8333333
median125
Q3138.7410714
95-th percentile159.1377467
Maximum203.775
Range149.2333333
Interquartile range (IQR)25.9077381

Descriptive statistics

Standard deviation18.61359086
Coefficient of variation (CV)0.1472472215
Kurtosis-0.03379315869
Mean126.4104727
Median Absolute Deviation (MAD)12.78947368
Skewness0.3318814023
Sum2525175.602
Variance346.4657649
MonotonicityNot monotonic
2021-11-29T11:23:34.802915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12821
 
0.1%
11617
 
0.1%
12717
 
0.1%
12217
 
0.1%
12517
 
0.1%
14216
 
0.1%
11016
 
0.1%
116.516
 
0.1%
13316
 
0.1%
12415
 
0.1%
Other values (15743)19808
99.0%
(Missing)24
 
0.1%
ValueCountFrequency (%)
54.541666671
< 0.1%
56.298076921
< 0.1%
57.5751
< 0.1%
60.346153851
< 0.1%
651
< 0.1%
65.1251
< 0.1%
65.270833331
< 0.1%
661
< 0.1%
66.090909091
< 0.1%
68.51
< 0.1%
ValueCountFrequency (%)
203.7751
< 0.1%
200.78301891
< 0.1%
200.15555561
< 0.1%
197.21
< 0.1%
197.14285711
< 0.1%
196.80434781
< 0.1%
194.18627451
< 0.1%
192.751
< 0.1%
192.43965521
< 0.1%
192.18571431
< 0.1%

MAP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14332
Distinct (%)72.0%
Missing102
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean86.74191272
Minimum34.29166667
Maximum143.2285714
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:34.908589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum34.29166667
5-th percentile68.62435345
Q177.26923077
median85.13126566
Q394.64404762
95-th percentile110.3827381
Maximum143.2285714
Range108.9369048
Interquartile range (IQR)17.37481685

Descriptive statistics

Standard deviation12.90803422
Coefficient of variation (CV)0.1488096564
Kurtosis0.3753422494
Mean86.74191272
Median Absolute Deviation (MAD)8.535401003
Skewness0.6007607492
Sum1725990.579
Variance166.6173475
MonotonicityNot monotonic
2021-11-29T11:23:35.013715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8534
 
0.2%
8031
 
0.2%
8730
 
0.1%
9228
 
0.1%
9127
 
0.1%
8226
 
0.1%
9325
 
0.1%
8125
 
0.1%
79.523
 
0.1%
8922
 
0.1%
Other values (14322)19627
98.1%
(Missing)102
 
0.5%
ValueCountFrequency (%)
34.291666671
< 0.1%
38.468751
< 0.1%
45.791666671
< 0.1%
46.166666671
< 0.1%
46.956521741
< 0.1%
48.039473681
< 0.1%
48.51
< 0.1%
491
< 0.1%
50.056818181
< 0.1%
50.285714291
< 0.1%
ValueCountFrequency (%)
143.22857141
< 0.1%
141.06451611
< 0.1%
140.72413791
< 0.1%
140.40566041
< 0.1%
1401
< 0.1%
139.93751
< 0.1%
138.99074071
< 0.1%
138.13636361
< 0.1%
137.51
< 0.1%
137.41304351
< 0.1%

DBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14217
Distinct (%)71.2%
Missing27
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean66.6384943
Minimum24
Maximum116.1891892
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:35.116334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile50.94563492
Q159.1
median65.55
Q373.28571429
95-th percentile86.22745098
Maximum116.1891892
Range92.18918919
Interquartile range (IQR)14.18571429

Descriptive statistics

Standard deviation10.80896208
Coefficient of variation (CV)0.1622029758
Kurtosis0.5003833477
Mean66.6384943
Median Absolute Deviation (MAD)6.992307692
Skewness0.5108030707
Sum1330970.647
Variance116.8336612
MonotonicityNot monotonic
2021-11-29T11:23:35.219604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67.534
 
0.2%
6129
 
0.1%
6927
 
0.1%
6626
 
0.1%
6525
 
0.1%
7025
 
0.1%
6725
 
0.1%
6024
 
0.1%
6424
 
0.1%
6224
 
0.1%
Other values (14207)19710
98.6%
(Missing)27
 
0.1%
ValueCountFrequency (%)
241
< 0.1%
28.583333331
< 0.1%
29.5751
< 0.1%
30.414285711
< 0.1%
31.022222221
< 0.1%
32.022727271
< 0.1%
32.526315791
< 0.1%
32.93751
< 0.1%
33.088235291
< 0.1%
33.480769231
< 0.1%
ValueCountFrequency (%)
116.18918921
< 0.1%
116.01351351
< 0.1%
115.13636361
< 0.1%
114.8751
< 0.1%
113.28571431
< 0.1%
112.26923081
< 0.1%
110.8751
< 0.1%
110.8251
< 0.1%
110.03846151
< 0.1%
109.45348841
< 0.1%

Resp
Real number (ℝ≥0)

Distinct9074
Distinct (%)45.5%
Missing43
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean18.48372382
Minimum1
Maximum56.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:35.319953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.27119691
Q116.66666667
median18.1875
Q320.03947368
95-th percentile23.625
Maximum56.25
Range55.25
Interquartile range (IQR)3.372807018

Descriptive statistics

Standard deviation3.134884136
Coefficient of variation (CV)0.1696024117
Kurtosis6.795482191
Mean18.48372382
Median Absolute Deviation (MAD)1.677083333
Skewness0.6931995319
Sum368879.6762
Variance9.827498548
MonotonicityNot monotonic
2021-11-29T11:23:35.413971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18298
 
1.5%
17142
 
0.7%
16140
 
0.7%
17.5119
 
0.6%
1992
 
0.5%
18.584
 
0.4%
16.578
 
0.4%
17.3333333376
 
0.4%
2059
 
0.3%
19.553
 
0.3%
Other values (9064)18816
94.1%
ValueCountFrequency (%)
14
< 0.1%
1.3333333332
< 0.1%
1.52
< 0.1%
1.5833333331
 
< 0.1%
1.6363636361
 
< 0.1%
1.6428571431
 
< 0.1%
1.7647058821
 
< 0.1%
1.8333333331
 
< 0.1%
1.8421052631
 
< 0.1%
1.91
 
< 0.1%
ValueCountFrequency (%)
56.251
< 0.1%
561
< 0.1%
50.041666671
< 0.1%
44.285714291
< 0.1%
40.214285711
< 0.1%
39.51
< 0.1%
38.916666671
< 0.1%
37.51251
< 0.1%
37.378787881
< 0.1%
37.251
< 0.1%

EtCO2
Real number (ℝ≥0)

MISSING

Distinct1928
Distinct (%)60.0%
Missing16784
Missing (%)83.9%
Infinite0
Infinite (%)0.0%
Mean33.09096109
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:35.518353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile17.8875
Q128.34903846
median33.05
Q337.25
95-th percentile43.25347222
Maximum100
Range90
Interquartile range (IQR)8.900961538

Descriptive statistics

Standard deviation10.19338005
Coefficient of variation (CV)0.3080412207
Kurtosis16.96107567
Mean33.09096109
Median Absolute Deviation (MAD)4.45
Skewness2.739708572
Sum106420.5309
Variance103.9049968
MonotonicityNot monotonic
2021-11-29T11:23:35.614874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3528
 
0.1%
3426
 
0.1%
4023
 
0.1%
3322
 
0.1%
3621
 
0.1%
3920
 
0.1%
3219
 
0.1%
2819
 
0.1%
3719
 
0.1%
34.518
 
0.1%
Other values (1918)3001
 
15.0%
(Missing)16784
83.9%
ValueCountFrequency (%)
104
< 0.1%
10.1251
 
< 0.1%
10.55
< 0.1%
10.833333331
 
< 0.1%
116
< 0.1%
11.151
 
< 0.1%
11.251
 
< 0.1%
11.51
 
< 0.1%
11.751
 
< 0.1%
11.846153851
 
< 0.1%
ValueCountFrequency (%)
1007
< 0.1%
994
< 0.1%
98.51
 
< 0.1%
986
< 0.1%
97.51
 
< 0.1%
97.251
 
< 0.1%
977
< 0.1%
962
 
< 0.1%
951
 
< 0.1%
942
 
< 0.1%

BaseExcess
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct392
Distinct (%)70.3%
Missing19442
Missing (%)97.2%
Infinite0
Infinite (%)0.0%
Mean-2.529682715
Minimum-22.36666667
Maximum9.1
Zeros2
Zeros (%)< 0.1%
Negative434
Negative (%)2.2%
Memory size156.4 KiB
2021-11-29T11:23:35.716098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-22.36666667
5-th percentile-9.23
Q1-4.6
median-2.4
Q3-0.38125
95-th percentile3.6778125
Maximum9.1
Range31.46666667
Interquartile range (IQR)4.21875

Descriptive statistics

Standard deviation3.85935829
Coefficient of variation (CV)-1.525629387
Kurtosis2.348435347
Mean-2.529682715
Median Absolute Deviation (MAD)2.1
Skewness-0.4961503107
Sum-1411.562955
Variance14.89464641
MonotonicityNot monotonic
2021-11-29T11:23:35.811122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.78
 
< 0.1%
-1.37
 
< 0.1%
-3.97
 
< 0.1%
1.26
 
< 0.1%
-4.66
 
< 0.1%
-0.95
 
< 0.1%
-3.35
 
< 0.1%
-3.75
 
< 0.1%
-4.45
 
< 0.1%
-3.45
 
< 0.1%
Other values (382)499
 
2.5%
(Missing)19442
97.2%
ValueCountFrequency (%)
-22.366666671
< 0.1%
-18.251
< 0.1%
-16.183333331
< 0.1%
-15.11
< 0.1%
-15.0751
< 0.1%
-14.0251
< 0.1%
-14.021
< 0.1%
-13.051
< 0.1%
-121
< 0.1%
-11.81
< 0.1%
ValueCountFrequency (%)
9.11
< 0.1%
91
< 0.1%
8.1751
< 0.1%
7.6428571431
< 0.1%
7.62
< 0.1%
7.11
< 0.1%
6.31
< 0.1%
6.11
< 0.1%
5.92
< 0.1%
5.851
< 0.1%

HCO3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct310
Distinct (%)74.5%
Missing19584
Missing (%)97.9%
Infinite0
Infinite (%)0.0%
Mean23.34547262
Minimum7.7
Maximum33.05714286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:35.910256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7.7
5-th percentile18.39375
Q121.7
median23.403125
Q325.0125
95-th percentile28.8
Maximum33.05714286
Range25.35714286
Interquartile range (IQR)3.3125

Descriptive statistics

Standard deviation3.07572166
Coefficient of variation (CV)0.1317480999
Kurtosis2.223522011
Mean23.34547262
Median Absolute Deviation (MAD)1.671875
Skewness-0.2459323095
Sum9711.71661
Variance9.460063729
MonotonicityNot monotonic
2021-11-29T11:23:36.009780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.56
 
< 0.1%
21.56
 
< 0.1%
23.95
 
< 0.1%
23.25
 
< 0.1%
28.84
 
< 0.1%
22.64
 
< 0.1%
21.34
 
< 0.1%
224
 
< 0.1%
21.74
 
< 0.1%
22.24
 
< 0.1%
Other values (300)370
 
1.8%
(Missing)19584
97.9%
ValueCountFrequency (%)
7.71
< 0.1%
13.11
< 0.1%
13.151
< 0.1%
14.61
< 0.1%
15.466666671
< 0.1%
16.21
< 0.1%
16.366666671
< 0.1%
16.431
< 0.1%
16.81
< 0.1%
171
< 0.1%
ValueCountFrequency (%)
33.057142861
< 0.1%
32.81
< 0.1%
32.41
< 0.1%
322
< 0.1%
30.91
< 0.1%
30.61
< 0.1%
30.11
< 0.1%
29.71
< 0.1%
29.52
< 0.1%
29.451
< 0.1%

FiO2
Real number (ℝ)

MISSING
SKEWED

Distinct620
Distinct (%)10.6%
Missing14178
Missing (%)70.9%
Infinite0
Infinite (%)0.0%
Mean0.6540048137
Minimum-16
Maximum1000.3
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size156.4 KiB
2021-11-29T11:23:36.186614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-16
5-th percentile0.21
Q10.36
median0.44
Q30.5891865079
95-th percentile1
Maximum1000.3
Range1016.3
Interquartile range (IQR)0.2291865079

Descriptive statistics

Standard deviation13.10707758
Coefficient of variation (CV)20.04125551
Kurtosis5815.504986
Mean0.6540048137
Median Absolute Deviation (MAD)0.105
Skewness76.23783285
Sum3807.616025
Variance171.7954826
MonotonicityNot monotonic
2021-11-29T11:23:36.287518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4948
 
4.7%
0.21560
 
2.8%
0.5536
 
2.7%
1326
 
1.6%
0.7211
 
1.1%
0.45205
 
1.0%
0.6203
 
1.0%
0.28193
 
1.0%
0.3151
 
0.8%
0.35108
 
0.5%
Other values (610)2381
 
11.9%
(Missing)14178
70.9%
ValueCountFrequency (%)
-161
 
< 0.1%
-5.63751
 
< 0.1%
0.042
 
< 0.1%
0.054
 
< 0.1%
0.064
 
< 0.1%
0.1151
 
< 0.1%
0.161
 
< 0.1%
0.21560
2.8%
0.2125
 
0.1%
0.21304347831
 
< 0.1%
ValueCountFrequency (%)
1000.31
 
< 0.1%
24
 
< 0.1%
1.61
 
< 0.1%
1.52
 
< 0.1%
1.2666666671
 
< 0.1%
1.2142857141
 
< 0.1%
1.21
 
< 0.1%
1.1666666672
 
< 0.1%
1326
1.6%
0.991
 
< 0.1%

pH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct761
Distinct (%)13.2%
Missing14246
Missing (%)71.2%
Infinite0
Infinite (%)0.0%
Mean7.378449701
Minimum6.78
Maximum7.615
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:36.391679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6.78
5-th percentile7.27
Q17.34
median7.38
Q37.42
95-th percentile7.485
Maximum7.615
Range0.835
Interquartile range (IQR)0.08

Descriptive statistics

Standard deviation0.06838952578
Coefficient of variation (CV)0.009268820491
Kurtosis3.555423499
Mean7.378449701
Median Absolute Deviation (MAD)0.04
Skewness-0.6345560451
Sum42455.59958
Variance0.004677127236
MonotonicityNot monotonic
2021-11-29T11:23:36.489905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.38261
 
1.3%
7.4205
 
1.0%
7.37199
 
1.0%
7.41196
 
1.0%
7.42194
 
1.0%
7.36191
 
1.0%
7.34184
 
0.9%
7.35180
 
0.9%
7.39163
 
0.8%
7.32156
 
0.8%
Other values (751)3825
 
19.1%
(Missing)14246
71.2%
ValueCountFrequency (%)
6.781
< 0.1%
6.811
< 0.1%
6.9866666671
< 0.1%
7.0033333331
< 0.1%
7.0266666671
< 0.1%
7.0343751
< 0.1%
7.0466666671
< 0.1%
7.058751
< 0.1%
7.061
< 0.1%
7.068751
< 0.1%
ValueCountFrequency (%)
7.6151
 
< 0.1%
7.611
 
< 0.1%
7.64
< 0.1%
7.593
< 0.1%
7.5852
< 0.1%
7.5833333331
 
< 0.1%
7.583
< 0.1%
7.5751
 
< 0.1%
7.5741
 
< 0.1%
7.573
< 0.1%

PaCO2
Real number (ℝ≥0)

MISSING

Distinct1324
Distinct (%)22.9%
Missing14221
Missing (%)71.1%
Infinite0
Infinite (%)0.0%
Mean40.36314483
Minimum12
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:36.592665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile28.5
Q135
median39
Q343.56666667
95-th percentile57
Maximum100
Range88
Interquartile range (IQR)8.566666667

Descriptive statistics

Standard deviation9.502627724
Coefficient of variation (CV)0.2354283286
Kurtosis6.261454088
Mean40.36314483
Median Absolute Deviation (MAD)4
Skewness1.836396002
Sum233258.6139
Variance90.29993367
MonotonicityNot monotonic
2021-11-29T11:23:36.689623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38173
 
0.9%
36155
 
0.8%
35150
 
0.8%
37150
 
0.8%
40150
 
0.8%
34146
 
0.7%
41137
 
0.7%
42135
 
0.7%
39124
 
0.6%
44119
 
0.6%
Other values (1314)4340
 
21.7%
(Missing)14221
71.1%
ValueCountFrequency (%)
121
 
< 0.1%
153
< 0.1%
15.31
 
< 0.1%
15.51
 
< 0.1%
162
< 0.1%
16.71
 
< 0.1%
173
< 0.1%
184
< 0.1%
18.81
 
< 0.1%
194
< 0.1%
ValueCountFrequency (%)
1001
< 0.1%
95.751
< 0.1%
951
< 0.1%
94.666666671
< 0.1%
93.41
< 0.1%
92.52
< 0.1%
921
< 0.1%
89.666666671
< 0.1%
89.21
< 0.1%
88.11
< 0.1%

SaO2
Real number (ℝ≥0)

MISSING

Distinct1153
Distinct (%)22.5%
Missing14875
Missing (%)74.4%
Infinite0
Infinite (%)0.0%
Mean96.68643136
Minimum50.3
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:36.789748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum50.3
5-th percentile91.9
Q195.8
median97.4
Q398.6
95-th percentile99.5
Maximum100
Range49.7
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation3.121416242
Coefficient of variation (CV)0.03228391201
Kurtosis38.47620369
Mean96.68643136
Median Absolute Deviation (MAD)1.3
Skewness-4.428625065
Sum495517.9607
Variance9.743239358
MonotonicityNot monotonic
2021-11-29T11:23:36.893323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9988
 
0.4%
98.886
 
0.4%
99.280
 
0.4%
98.680
 
0.4%
98.273
 
0.4%
99.172
 
0.4%
98.772
 
0.4%
99.470
 
0.4%
97.869
 
0.3%
98.468
 
0.3%
Other values (1143)4367
 
21.8%
(Missing)14875
74.4%
ValueCountFrequency (%)
50.31
< 0.1%
52.51
< 0.1%
58.81
< 0.1%
65.11
< 0.1%
65.61
< 0.1%
66.11
< 0.1%
681
< 0.1%
68.21
< 0.1%
68.251
< 0.1%
69.466666671
< 0.1%
ValueCountFrequency (%)
1001
 
< 0.1%
99.930
0.1%
99.851
 
< 0.1%
99.838461541
 
< 0.1%
99.833333333
 
< 0.1%
99.82
 
< 0.1%
99.834
0.2%
99.7751
 
< 0.1%
99.753
 
< 0.1%
99.738888891
 
< 0.1%

AST
Real number (ℝ≥0)

MISSING

Distinct1140
Distinct (%)13.5%
Missing11536
Missing (%)57.7%
Infinite0
Infinite (%)0.0%
Mean102.5666227
Minimum5
Maximum9264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:36.994787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile13
Q119
median28
Q353
95-th percentile288.925
Maximum9264
Range9259
Interquartile range (IQR)34

Descriptive statistics

Standard deviation408.9595643
Coefficient of variation (CV)3.987257781
Kurtosis147.3602341
Mean102.5666227
Median Absolute Deviation (MAD)11.5
Skewness10.87603604
Sum868123.8943
Variance167247.9253
MonotonicityNot monotonic
2021-11-29T11:23:37.095334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18263
 
1.3%
19262
 
1.3%
17254
 
1.3%
21245
 
1.2%
20241
 
1.2%
16228
 
1.1%
22219
 
1.1%
24214
 
1.1%
15213
 
1.1%
23197
 
1.0%
Other values (1130)6128
30.6%
(Missing)11536
57.7%
ValueCountFrequency (%)
52
 
< 0.1%
5.51
 
< 0.1%
65
 
< 0.1%
6.52
 
< 0.1%
77
 
< 0.1%
815
0.1%
8.251
 
< 0.1%
8.56
 
< 0.1%
933
0.2%
9.511
 
0.1%
ValueCountFrequency (%)
92641
< 0.1%
79061
< 0.1%
68361
< 0.1%
6642.21
< 0.1%
6602.51
< 0.1%
65601
< 0.1%
62361
< 0.1%
6069.61
< 0.1%
5866.51
< 0.1%
5805.6666671
< 0.1%

BUN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1122
Distinct (%)6.1%
Missing1591
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean21.43511645
Minimum1
Maximum192.8333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:37.198686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q111
median16
Q325
95-th percentile57
Maximum192.8333333
Range191.8333333
Interquartile range (IQR)14

Descriptive statistics

Standard deviation17.55304021
Coefficient of variation (CV)0.8188917588
Kurtosis10.36440433
Mean21.43511645
Median Absolute Deviation (MAD)6
Skewness2.69803319
Sum394599.0587
Variance308.1092206
MonotonicityNot monotonic
2021-11-29T11:23:37.301141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13653
 
3.3%
12651
 
3.3%
10601
 
3.0%
14588
 
2.9%
11576
 
2.9%
15519
 
2.6%
16495
 
2.5%
9491
 
2.5%
17471
 
2.4%
8429
 
2.1%
Other values (1112)12935
64.7%
(Missing)1591
 
8.0%
ValueCountFrequency (%)
16
 
< 0.1%
1.251
 
< 0.1%
1.55
 
< 0.1%
1.5384615381
 
< 0.1%
1.5833333332
 
< 0.1%
1.6666666676
 
< 0.1%
1.751
 
< 0.1%
216
0.1%
2.1251
 
< 0.1%
2.3333333331
 
< 0.1%
ValueCountFrequency (%)
192.83333331
< 0.1%
181.61
< 0.1%
179.51
< 0.1%
1781
< 0.1%
1721
< 0.1%
1701
< 0.1%
1611
< 0.1%
1521
< 0.1%
151.33333331
< 0.1%
1471
< 0.1%

Alkalinephos
Real number (ℝ≥0)

MISSING

Distinct1036
Distinct (%)12.2%
Missing11530
Missing (%)57.6%
Infinite0
Infinite (%)0.0%
Mean88.37053948
Minimum11
Maximum1650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:37.407818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile34.05
Q152
median69
Q397
95-th percentile198
Maximum1650
Range1639
Interquartile range (IQR)45

Descriptive statistics

Standard deviation80.74393553
Coefficient of variation (CV)0.9136974382
Kurtosis74.15927109
Mean88.37053948
Median Absolute Deviation (MAD)20
Skewness6.637635128
Sum748498.4694
Variance6519.583125
MonotonicityNot monotonic
2021-11-29T11:23:37.506451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52118
 
0.6%
53117
 
0.6%
69114
 
0.6%
55113
 
0.6%
58110
 
0.5%
56108
 
0.5%
50108
 
0.5%
71107
 
0.5%
54107
 
0.5%
59106
 
0.5%
Other values (1026)7362
36.8%
(Missing)11530
57.6%
ValueCountFrequency (%)
112
< 0.1%
131
 
< 0.1%
141
 
< 0.1%
151
 
< 0.1%
162
< 0.1%
184
< 0.1%
18.333333331
 
< 0.1%
194
< 0.1%
19.333333331
 
< 0.1%
202
< 0.1%
ValueCountFrequency (%)
16502
< 0.1%
1303.51
< 0.1%
12141
< 0.1%
11291
< 0.1%
10721
< 0.1%
1051.51
< 0.1%
1003.251
< 0.1%
9721
< 0.1%
959.51
< 0.1%
9581
< 0.1%

Calcium
Real number (ℝ≥0)

MISSING

Distinct3769
Distinct (%)20.4%
Missing1550
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean7.743981929
Minimum1.063333333
Maximum27.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:37.689714image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.063333333
5-th percentile4.035714286
Q17.3
median8.266666667
Q38.766666667
95-th percentile9.4
Maximum27.45
Range26.38666667
Interquartile range (IQR)1.466666667

Descriptive statistics

Standard deviation1.77068401
Coefficient of variation (CV)0.2286529109
Kurtosis4.496435604
Mean7.743981929
Median Absolute Deviation (MAD)0.6166666667
Skewness-0.5413985796
Sum142876.4666
Variance3.135321863
MonotonicityNot monotonic
2021-11-29T11:23:37.788487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.5647
 
3.2%
8.3549
 
2.7%
8.6542
 
2.7%
8.4513
 
2.6%
8.8501
 
2.5%
8.7497
 
2.5%
8.2479
 
2.4%
8.9445
 
2.2%
8.1441
 
2.2%
9433
 
2.2%
Other values (3759)13403
67.0%
(Missing)1550
 
7.8%
ValueCountFrequency (%)
1.0633333331
 
< 0.1%
1.071
 
< 0.1%
1.083
< 0.1%
1.0851
 
< 0.1%
1.091
 
< 0.1%
1.12
< 0.1%
1.111
 
< 0.1%
1.121
 
< 0.1%
1.1251
 
< 0.1%
1.133
< 0.1%
ValueCountFrequency (%)
27.451
 
< 0.1%
25.21
 
< 0.1%
23.71
 
< 0.1%
19.11
 
< 0.1%
18.82
< 0.1%
18.63
< 0.1%
18.31
 
< 0.1%
18.23
< 0.1%
18.11
 
< 0.1%
182
< 0.1%

Chloride
Real number (ℝ≥0)

MISSING

Distinct321
Distinct (%)19.9%
Missing18383
Missing (%)91.9%
Infinite0
Infinite (%)0.0%
Mean105.9066444
Minimum74
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:37.884702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum74
5-th percentile97
Q1103.5
median106.2857143
Q3109
95-th percentile113
Maximum124
Range50
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation5.04682118
Coefficient of variation (CV)0.04765348961
Kurtosis3.06717699
Mean105.9066444
Median Absolute Deviation (MAD)2.714285714
Skewness-0.8022249078
Sum171251.0441
Variance25.47040402
MonotonicityNot monotonic
2021-11-29T11:23:37.985261image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107100
 
0.5%
10898
 
0.5%
10696
 
0.5%
10594
 
0.5%
10980
 
0.4%
10378
 
0.4%
10473
 
0.4%
11059
 
0.3%
10251
 
0.3%
11134
 
0.2%
Other values (311)854
 
4.3%
(Missing)18383
91.9%
ValueCountFrequency (%)
741
 
< 0.1%
821
 
< 0.1%
82.51
 
< 0.1%
83.333333331
 
< 0.1%
851
 
< 0.1%
862
< 0.1%
884
< 0.1%
88.777777781
 
< 0.1%
893
< 0.1%
89.51
 
< 0.1%
ValueCountFrequency (%)
1243
< 0.1%
1232
 
< 0.1%
1221
 
< 0.1%
121.66666671
 
< 0.1%
1192
 
< 0.1%
118.66666671
 
< 0.1%
118.52
 
< 0.1%
1185
< 0.1%
117.51
 
< 0.1%
117.21
 
< 0.1%

Creatinine
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3604
Distinct (%)19.6%
Missing1588
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean1.576464298
Minimum0.2
Maximum25.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:38.086174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.53
Q10.735
median0.94
Q31.355
95-th percentile5.71
Maximum25.33
Range25.13
Interquartile range (IQR)0.62

Descriptive statistics

Standard deviation2.083636156
Coefficient of variation (CV)1.321714775
Kurtosis24.80467251
Mean1.576464298
Median Absolute Deviation (MAD)0.2566666667
Skewness4.413790644
Sum29025.86066
Variance4.341539629
MonotonicityNot monotonic
2021-11-29T11:23:38.185174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.81168
 
0.8%
0.8156
 
0.8%
0.77154
 
0.8%
0.79154
 
0.8%
0.73153
 
0.8%
0.71150
 
0.8%
0.75145
 
0.7%
0.7143
 
0.7%
0.89138
 
0.7%
0.78136
 
0.7%
Other values (3594)16915
84.6%
(Missing)1588
 
7.9%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.222
 
< 0.1%
0.2351
 
< 0.1%
0.241
 
< 0.1%
0.271
 
< 0.1%
0.281
 
< 0.1%
0.2851
 
< 0.1%
0.332
0.2%
0.3051
 
< 0.1%
0.314
 
< 0.1%
ValueCountFrequency (%)
25.331
< 0.1%
251
< 0.1%
24.571
< 0.1%
23.831
< 0.1%
23.8251
< 0.1%
23.4951
< 0.1%
23.4851
< 0.1%
23.4351
< 0.1%
21.311
< 0.1%
21.181
< 0.1%

Bilirubin_direct
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct218
Distinct (%)14.8%
Missing18529
Missing (%)92.6%
Infinite0
Infinite (%)0.0%
Mean0.7189215335
Minimum0.01
Maximum24.035
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:38.282674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.1
Q10.1
median0.2
Q30.43
95-th percentile2.5
Maximum24.035
Range24.025
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation2.022711854
Coefficient of variation (CV)2.813536332
Kurtosis59.1130836
Mean0.7189215335
Median Absolute Deviation (MAD)0.1
Skewness7.079414699
Sum1057.533576
Variance4.091363246
MonotonicityNot monotonic
2021-11-29T11:23:38.384832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1425
 
2.1%
0.2262
 
1.3%
0.3118
 
0.6%
0.480
 
0.4%
0.535
 
0.2%
0.627
 
0.1%
119
 
0.1%
0.717
 
0.1%
0.2515
 
0.1%
0.813
 
0.1%
Other values (208)460
 
2.3%
(Missing)18529
92.6%
ValueCountFrequency (%)
0.015
< 0.1%
0.024
< 0.1%
0.035
< 0.1%
0.044
< 0.1%
0.051
 
< 0.1%
0.054
< 0.1%
0.066
< 0.1%
0.061
 
< 0.1%
0.077
< 0.1%
0.084
< 0.1%
ValueCountFrequency (%)
24.0351
< 0.1%
21.3351
< 0.1%
20.571
< 0.1%
202
< 0.1%
19.8151
< 0.1%
18.3951
< 0.1%
17.951
< 0.1%
16.61
< 0.1%
15.61
< 0.1%
15.21
< 0.1%

Glucose
Real number (ℝ≥0)

MISSING

Distinct7610
Distinct (%)40.4%
Missing1173
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean130.9364556
Minimum44
Maximum444.6666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:38.483387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile88.5
Q1106.6
median124.2
Q3144.125
95-th percentile201.4143939
Maximum444.6666667
Range400.6666667
Interquartile range (IQR)37.525

Descriptive statistics

Standard deviation36.20263241
Coefficient of variation (CV)0.2764900902
Kurtosis5.310666208
Mean130.9364556
Median Absolute Deviation (MAD)18.55
Skewness1.733113816
Sum2465140.649
Variance1310.630593
MonotonicityNot monotonic
2021-11-29T11:23:38.581601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99109
 
0.5%
95101
 
0.5%
10194
 
0.5%
10292
 
0.5%
10686
 
0.4%
9685
 
0.4%
10484
 
0.4%
11883
 
0.4%
9283
 
0.4%
9482
 
0.4%
Other values (7600)17928
89.6%
(Missing)1173
 
5.9%
ValueCountFrequency (%)
441
 
< 0.1%
511
 
< 0.1%
59.166666671
 
< 0.1%
611
 
< 0.1%
61.51
 
< 0.1%
622
< 0.1%
641
 
< 0.1%
65.607142861
 
< 0.1%
663
< 0.1%
671
 
< 0.1%
ValueCountFrequency (%)
444.66666671
< 0.1%
439.8751
< 0.1%
425.16666671
< 0.1%
415.351
< 0.1%
412.406251
< 0.1%
401.51
< 0.1%
3921
< 0.1%
3891
< 0.1%
376.51
< 0.1%
365.33333331
< 0.1%

Lactate
Real number (ℝ≥0)

MISSING

Distinct2042
Distinct (%)42.9%
Missing15240
Missing (%)76.2%
Infinite0
Infinite (%)0.0%
Mean2.274262809
Minimum0.5
Maximum21.04363636
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:38.687794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.85
Q11.26
median1.73
Q32.621458333
95-th percentile5.416607143
Maximum21.04363636
Range20.54363636
Interquartile range (IQR)1.361458333

Descriptive statistics

Standard deviation1.818896201
Coefficient of variation (CV)0.7997739724
Kurtosis21.5552717
Mean2.274262809
Median Absolute Deviation (MAD)0.5766666667
Skewness3.769069307
Sum10825.49097
Variance3.30838339
MonotonicityNot monotonic
2021-11-29T11:23:38.782868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.234
 
0.2%
1.130
 
0.1%
1.330
 
0.1%
1.2328
 
0.1%
1.427
 
0.1%
127
 
0.1%
0.924
 
0.1%
1.4223
 
0.1%
1.3523
 
0.1%
1.3122
 
0.1%
Other values (2032)4492
 
22.5%
(Missing)15240
76.2%
ValueCountFrequency (%)
0.54
< 0.1%
0.541
 
< 0.1%
0.551
 
< 0.1%
0.562
 
< 0.1%
0.574
< 0.1%
0.591
 
< 0.1%
0.66
< 0.1%
0.611
 
< 0.1%
0.6151
 
< 0.1%
0.621
 
< 0.1%
ValueCountFrequency (%)
21.043636361
< 0.1%
19.4251
< 0.1%
19.121
< 0.1%
17.751
< 0.1%
17.421
< 0.1%
16.902666671
< 0.1%
16.731666671
< 0.1%
16.293333331
< 0.1%
15.941
< 0.1%
15.9351
< 0.1%

Magnesium
Real number (ℝ≥0)

MISSING

Distinct516
Distinct (%)3.1%
Missing3543
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean2.024972919
Minimum0.5
Maximum8.042857143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:38.879522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1.6
Q11.85
median2
Q32.166666667
95-th percentile2.5
Maximum8.042857143
Range7.542857143
Interquartile range (IQR)0.3166666667

Descriptive statistics

Standard deviation0.3178351334
Coefficient of variation (CV)0.1569577205
Kurtosis25.3527359
Mean2.024972919
Median Absolute Deviation (MAD)0.15
Skewness2.493858888
Sum33324.97932
Variance0.101019172
MonotonicityNot monotonic
2021-11-29T11:23:38.974123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21719
 
8.6%
1.91412
 
7.1%
2.11361
 
6.8%
1.81126
 
5.6%
2.2848
 
4.2%
1.7721
 
3.6%
2.3569
 
2.8%
2.05532
 
2.7%
1.85425
 
2.1%
1.95418
 
2.1%
Other values (506)7326
36.6%
(Missing)3543
17.7%
ValueCountFrequency (%)
0.51
 
< 0.1%
0.651
 
< 0.1%
0.92
 
< 0.1%
15
 
< 0.1%
1.051
 
< 0.1%
1.115
0.1%
1.152
 
< 0.1%
1.228
0.1%
1.21
 
< 0.1%
1.2333333331
 
< 0.1%
ValueCountFrequency (%)
8.0428571431
< 0.1%
7.2666666671
< 0.1%
6.351
< 0.1%
6.21
< 0.1%
6.0333333331
< 0.1%
5.341
< 0.1%
5.31
< 0.1%
5.0666666671
< 0.1%
51
< 0.1%
4.9166666671
< 0.1%

Phosphate
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct823
Distinct (%)7.1%
Missing8365
Missing (%)41.8%
Infinite0
Infinite (%)0.0%
Mean3.519953026
Minimum0.6
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:39.149074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile2
Q12.75
median3.3
Q34
95-th percentile5.85
Maximum12
Range11.4
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation1.240452874
Coefficient of variation (CV)0.3524060876
Kurtosis5.718643859
Mean3.519953026
Median Absolute Deviation (MAD)0.6
Skewness1.737128628
Sum40954.65345
Variance1.538723333
MonotonicityNot monotonic
2021-11-29T11:23:39.253240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3354
 
1.8%
3.5350
 
1.8%
3.4347
 
1.7%
3.2337
 
1.7%
3.3321
 
1.6%
3.1315
 
1.6%
2.9314
 
1.6%
2.8287
 
1.4%
3.6279
 
1.4%
3.7271
 
1.4%
Other values (813)8460
42.3%
(Missing)8365
41.8%
ValueCountFrequency (%)
0.63
 
< 0.1%
0.651
 
< 0.1%
0.71
 
< 0.1%
0.87
< 0.1%
0.852
 
< 0.1%
0.881
 
< 0.1%
0.91
 
< 0.1%
113
0.1%
1.0333333331
 
< 0.1%
1.16
< 0.1%
ValueCountFrequency (%)
126
< 0.1%
11.851
 
< 0.1%
11.81
 
< 0.1%
11.61
 
< 0.1%
11.51
 
< 0.1%
11.42
 
< 0.1%
11.31
 
< 0.1%
11.21
 
< 0.1%
11.133333331
 
< 0.1%
111
 
< 0.1%

Potassium
Real number (ℝ≥0)

MISSING

Distinct2065
Distinct (%)11.1%
Missing1434
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean4.053526956
Minimum1.825
Maximum9.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:39.355941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.825
5-th percentile3.366666667
Q13.75
median4
Q34.3
95-th percentile4.928961039
Maximum9.8
Range7.975
Interquartile range (IQR)0.55

Descriptive statistics

Standard deviation0.5033147111
Coefficient of variation (CV)0.124167106
Kurtosis7.23620474
Mean4.053526956
Median Absolute Deviation (MAD)0.2872115385
Skewness1.397132282
Sum75257.78146
Variance0.2533256984
MonotonicityNot monotonic
2021-11-29T11:23:39.457599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4966
 
4.8%
3.9857
 
4.3%
3.8768
 
3.8%
4.1765
 
3.8%
3.7645
 
3.2%
4.2562
 
2.8%
3.6542
 
2.7%
4.3474
 
2.4%
3.5436
 
2.2%
4.4383
 
1.9%
Other values (2055)12168
60.8%
(Missing)1434
 
7.2%
ValueCountFrequency (%)
1.8251
 
< 0.1%
2.33
< 0.1%
2.3251
 
< 0.1%
2.41
 
< 0.1%
2.481
 
< 0.1%
2.52
< 0.1%
2.5333333331
 
< 0.1%
2.553
< 0.1%
2.5666666671
 
< 0.1%
2.63
< 0.1%
ValueCountFrequency (%)
9.81
 
< 0.1%
9.42
< 0.1%
9.22
< 0.1%
8.4751
 
< 0.1%
8.21
 
< 0.1%
7.91
 
< 0.1%
7.81
 
< 0.1%
7.63
< 0.1%
7.5951
 
< 0.1%
7.41
 
< 0.1%

Bilirubin_total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct556
Distinct (%)6.6%
Missing11522
Missing (%)57.6%
Infinite0
Infinite (%)0.0%
Mean1.254390975
Minimum0.1
Maximum49.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:39.562308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q10.5
median0.8
Q31.2
95-th percentile3.121964286
Maximum49.2
Range49.1
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation2.316173178
Coefficient of variation (CV)1.846452361
Kurtosis121.4421744
Mean1.254390975
Median Absolute Deviation (MAD)0.3
Skewness9.514188877
Sum10634.72669
Variance5.364658191
MonotonicityNot monotonic
2021-11-29T11:23:39.663901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6739
 
3.7%
0.5734
 
3.7%
0.7688
 
3.4%
0.4653
 
3.3%
0.8561
 
2.8%
0.9470
 
2.4%
1379
 
1.9%
0.3359
 
1.8%
1.1281
 
1.4%
1.2219
 
1.1%
Other values (546)3395
 
17.0%
(Missing)11522
57.6%
ValueCountFrequency (%)
0.130
 
0.1%
0.13333333331
 
< 0.1%
0.151
 
< 0.1%
0.152
 
< 0.1%
0.21
 
< 0.1%
0.2150
0.8%
0.23333333331
 
< 0.1%
0.2519
 
0.1%
0.26666666672
 
< 0.1%
0.2751
 
< 0.1%
ValueCountFrequency (%)
49.21
< 0.1%
45.433333331
< 0.1%
41.61
< 0.1%
39.241
< 0.1%
36.851
< 0.1%
35.51
< 0.1%
35.41
< 0.1%
34.651
< 0.1%
30.9251
< 0.1%
29.41
< 0.1%

TroponinI
Real number (ℝ≥0)

MISSING

Distinct1916
Distinct (%)29.2%
Missing13436
Missing (%)67.2%
Infinite0
Infinite (%)0.0%
Mean5.111238634
Minimum0.01
Maximum409.6833333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:39.766676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.03
median0.08
Q30.8
95-th percentile31.83739286
Maximum409.6833333
Range409.6733333
Interquartile range (IQR)0.77

Descriptive statistics

Standard deviation19.08923165
Coefficient of variation (CV)3.734756488
Kurtosis77.5875291
Mean5.111238634
Median Absolute Deviation (MAD)0.07
Skewness7.324340532
Sum33550.17039
Variance364.3987648
MonotonicityNot monotonic
2021-11-29T11:23:39.863106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.011209
 
6.0%
0.03775
 
3.9%
0.02275
 
1.4%
0.04237
 
1.2%
0.05135
 
0.7%
0.07113
 
0.6%
0.06113
 
0.6%
0.0878
 
0.4%
0.0974
 
0.4%
0.156
 
0.3%
Other values (1906)3499
 
17.5%
(Missing)13436
67.2%
ValueCountFrequency (%)
0.011209
6.0%
0.011428571431
 
< 0.1%
0.0124
 
< 0.1%
0.01251
 
< 0.1%
0.0133333333315
 
0.1%
0.0142
 
< 0.1%
0.01554
 
0.3%
0.016666666679
 
< 0.1%
0.017142857141
 
< 0.1%
0.01751
 
< 0.1%
ValueCountFrequency (%)
409.68333331
 
< 0.1%
298.831
 
< 0.1%
219.2851
 
< 0.1%
2005
< 0.1%
187.85333331
 
< 0.1%
187.85251
 
< 0.1%
183.51
 
< 0.1%
181.66333331
 
< 0.1%
180.081
 
< 0.1%
177.63251
 
< 0.1%

Hct
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2935
Distinct (%)16.3%
Missing1953
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean32.49811369
Minimum9.3
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:39.961077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum9.3
5-th percentile23.13333333
Q127.63333333
median32.3
Q336.95
95-th percentile42.9
Maximum65
Range55.7
Interquartile range (IQR)9.316666667

Descriptive statistics

Standard deviation6.18521518
Coefficient of variation (CV)0.1903253598
Kurtosis-0.2909080435
Mean32.49811369
Median Absolute Deviation (MAD)4.65
Skewness0.2744730185
Sum586493.4577
Variance38.25688683
MonotonicityNot monotonic
2021-11-29T11:23:40.054785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.582
 
0.4%
3478
 
0.4%
35.572
 
0.4%
3570
 
0.4%
29.570
 
0.4%
33.369
 
0.3%
34.668
 
0.3%
3767
 
0.3%
3366
 
0.3%
34.266
 
0.3%
Other values (2925)17339
86.7%
(Missing)1953
 
9.8%
ValueCountFrequency (%)
9.31
< 0.1%
12.61
< 0.1%
13.31
< 0.1%
13.551
< 0.1%
14.61
< 0.1%
15.51
< 0.1%
161
< 0.1%
16.71
< 0.1%
16.933333331
< 0.1%
17.251
< 0.1%
ValueCountFrequency (%)
651
< 0.1%
64.21
< 0.1%
64.11
< 0.1%
59.851
< 0.1%
59.81
< 0.1%
58.151
< 0.1%
57.166666671
< 0.1%
56.31
< 0.1%
55.7251
< 0.1%
55.61
< 0.1%

Hgb
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1676
Distinct (%)9.3%
Missing1941
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean10.67069996
Minimum2.6
Maximum26.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:40.152821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile7.55
Q18.981666667
median10.5
Q312.2
95-th percentile14.4
Maximum26.6
Range24
Interquartile range (IQR)3.218333333

Descriptive statistics

Standard deviation2.145317107
Coefficient of variation (CV)0.2010474585
Kurtosis0.3100678804
Mean10.67069996
Median Absolute Deviation (MAD)1.6
Skewness0.4759499628
Sum192702.1706
Variance4.602385491
MonotonicityNot monotonic
2021-11-29T11:23:40.252907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.3199
 
1.0%
10.5189
 
0.9%
11.2182
 
0.9%
11181
 
0.9%
10.6173
 
0.9%
9.5172
 
0.9%
11.8171
 
0.9%
11.7169
 
0.8%
11.5165
 
0.8%
10.9165
 
0.8%
Other values (1666)16293
81.5%
(Missing)1941
 
9.7%
ValueCountFrequency (%)
2.61
< 0.1%
41
< 0.1%
4.31
< 0.1%
4.51
< 0.1%
4.81
< 0.1%
4.91
< 0.1%
51
< 0.1%
5.11
< 0.1%
5.1333333331
< 0.1%
5.2333333331
< 0.1%
ValueCountFrequency (%)
26.61
< 0.1%
24.82
< 0.1%
23.81
< 0.1%
23.51
< 0.1%
22.51
< 0.1%
21.61
< 0.1%
21.21
< 0.1%
211
< 0.1%
20.61
< 0.1%
20.551
< 0.1%

PTT
Real number (ℝ≥0)

MISSING

Distinct1291
Distinct (%)29.4%
Missing15602
Missing (%)78.0%
Infinite0
Infinite (%)0.0%
Mean39.51081585
Minimum20
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:40.351764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile24
Q128.1
median31.56071429
Q338.2
95-th percentile84.2
Maximum250
Range230
Interquartile range (IQR)10.1

Descriptive statistics

Standard deviation26.94099795
Coefficient of variation (CV)0.6818638737
Kurtosis24.01269074
Mean39.51081585
Median Absolute Deviation (MAD)4.260714286
Skewness4.399903085
Sum173768.5681
Variance725.8173705
MonotonicityNot monotonic
2021-11-29T11:23:40.451008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2837
 
0.2%
30.735
 
0.2%
27.335
 
0.2%
28.634
 
0.2%
28.934
 
0.2%
28.733
 
0.2%
29.533
 
0.2%
28.533
 
0.2%
30.432
 
0.2%
28.831
 
0.2%
Other values (1281)4061
 
20.3%
(Missing)15602
78.0%
ValueCountFrequency (%)
2021
0.1%
20.13
 
< 0.1%
20.31
 
< 0.1%
20.43
 
< 0.1%
20.52
 
< 0.1%
20.62
 
< 0.1%
20.71
 
< 0.1%
20.84
 
< 0.1%
20.95
 
< 0.1%
214
 
< 0.1%
ValueCountFrequency (%)
2503
 
< 0.1%
249.94
 
< 0.1%
24912
0.1%
237.51
 
< 0.1%
234.351
 
< 0.1%
212.71666671
 
< 0.1%
212.31
 
< 0.1%
2061
 
< 0.1%
204.91
 
< 0.1%
203.41251
 
< 0.1%

WBC
Real number (ℝ≥0)

MISSING

Distinct2359
Distinct (%)13.1%
Missing2000
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean10.31449188
Minimum0.1
Maximum328.3666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:40.627790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile4.4
Q17.05
median9.45
Q312.35
95-th percentile18.5
Maximum328.3666667
Range328.2666667
Interquartile range (IQR)5.3

Descriptive statistics

Standard deviation6.47318033
Coefficient of variation (CV)0.6275811167
Kurtosis495.4497009
Mean10.31449188
Median Absolute Deviation (MAD)2.616666667
Skewness14.65031786
Sum185660.8538
Variance41.90206358
MonotonicityNot monotonic
2021-11-29T11:23:40.722802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.8135
 
0.7%
7.4124
 
0.6%
7120
 
0.6%
7.5118
 
0.6%
8.2117
 
0.6%
7.6117
 
0.6%
9.4116
 
0.6%
7.2116
 
0.6%
8116
 
0.6%
8.6116
 
0.6%
Other values (2349)16805
84.0%
(Missing)2000
 
10.0%
ValueCountFrequency (%)
0.16
< 0.1%
0.13
< 0.1%
0.16666666671
 
< 0.1%
0.22
 
< 0.1%
0.33
< 0.1%
0.31
 
< 0.1%
0.45
< 0.1%
0.43333333331
 
< 0.1%
0.51
 
< 0.1%
0.62
 
< 0.1%
ValueCountFrequency (%)
328.36666671
< 0.1%
200.81
< 0.1%
186.1251
< 0.1%
168.121
< 0.1%
163.561
< 0.1%
152.91
< 0.1%
144.91
< 0.1%
142.21
< 0.1%
136.91
< 0.1%
131.03333331
< 0.1%

Fibrinogen
Real number (ℝ≥0)

MISSING

Distinct955
Distinct (%)49.0%
Missing18052
Missing (%)90.3%
Infinite0
Infinite (%)0.0%
Mean294.67792
Minimum35
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:40.826565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile136.21
Q1202
median260.1666667
Q3349.625
95-th percentile583.65
Maximum1000
Range965
Interquartile range (IQR)147.625

Descriptive statistics

Standard deviation142.6140188
Coefficient of variation (CV)0.483965744
Kurtosis3.77128363
Mean294.67792
Median Absolute Deviation (MAD)68.44047619
Skewness1.660304846
Sum574032.5881
Variance20338.75835
MonotonicityNot monotonic
2021-11-29T11:23:40.928382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21718
 
0.1%
23012
 
0.1%
20012
 
0.1%
20311
 
0.1%
21910
 
0.1%
20810
 
0.1%
21510
 
0.1%
23210
 
0.1%
2849
 
< 0.1%
2349
 
< 0.1%
Other values (945)1837
 
9.2%
(Missing)18052
90.3%
ValueCountFrequency (%)
351
< 0.1%
55.51
< 0.1%
56.51
< 0.1%
59.51
< 0.1%
611
< 0.1%
641
< 0.1%
651
< 0.1%
67.333333331
< 0.1%
701
< 0.1%
711
< 0.1%
ValueCountFrequency (%)
10006
< 0.1%
9541
 
< 0.1%
9451
 
< 0.1%
9191
 
< 0.1%
9121
 
< 0.1%
8881
 
< 0.1%
8821
 
< 0.1%
8781
 
< 0.1%
871.83333331
 
< 0.1%
8671
 
< 0.1%

Platelets
Real number (ℝ≥0)

MISSING

Distinct2817
Distinct (%)15.6%
Missing1992
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean201.0262893
Minimum4
Maximum2322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:41.028145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile78.84791667
Q1139.5
median189
Q3248
95-th percentile360.5
Maximum2322
Range2318
Interquartile range (IQR)108.5

Descriptive statistics

Standard deviation92.86094032
Coefficient of variation (CV)0.4619343105
Kurtosis19.09495413
Mean201.0262893
Median Absolute Deviation (MAD)54
Skewness1.932377351
Sum3620081.417
Variance8623.154238
MonotonicityNot monotonic
2021-11-29T11:23:41.131016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20677
 
0.4%
18274
 
0.4%
15871
 
0.4%
18670
 
0.4%
16268
 
0.3%
16767
 
0.3%
16867
 
0.3%
20066
 
0.3%
17366
 
0.3%
16566
 
0.3%
Other values (2807)17316
86.6%
(Missing)1992
 
10.0%
ValueCountFrequency (%)
42
< 0.1%
4.52
< 0.1%
53
< 0.1%
72
< 0.1%
81
 
< 0.1%
101
 
< 0.1%
113
< 0.1%
11.333333331
 
< 0.1%
11.51
 
< 0.1%
11.751
 
< 0.1%
ValueCountFrequency (%)
23221
< 0.1%
1022.6666671
< 0.1%
893.51
< 0.1%
876.66666671
< 0.1%
876.251
< 0.1%
848.66666671
< 0.1%
8382
< 0.1%
8311
< 0.1%
8291
< 0.1%
8221
< 0.1%

Age
Real number (ℝ≥0)

Distinct77
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.6488
Minimum14
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:41.234570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile30
Q150
median62
Q372
95-th percentile85
Maximum100
Range86
Interquartile range (IQR)22

Descriptive statistics

Standard deviation16.67181022
Coefficient of variation (CV)0.2748910155
Kurtosis-0.1562598942
Mean60.6488
Median Absolute Deviation (MAD)11
Skewness-0.2649819802
Sum1212976
Variance277.949256
MonotonicityNot monotonic
2021-11-29T11:23:41.329798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67572
 
2.9%
68539
 
2.7%
66512
 
2.6%
65510
 
2.5%
61498
 
2.5%
69495
 
2.5%
71481
 
2.4%
62477
 
2.4%
63470
 
2.4%
70467
 
2.3%
Other values (67)14979
74.9%
ValueCountFrequency (%)
142
 
< 0.1%
152
 
< 0.1%
165
 
< 0.1%
1713
 
0.1%
1832
 
0.2%
1954
0.3%
2065
0.3%
2199
0.5%
2259
0.3%
2377
0.4%
ValueCountFrequency (%)
100392
2.0%
89111
 
0.6%
88138
 
0.7%
87145
 
0.7%
86187
0.9%
85187
0.9%
84206
1.0%
83247
1.2%
82242
1.2%
81224
1.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
1.0
10732 
0.0
9268 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.010732
53.7%
0.09268
46.3%

Length

2021-11-29T11:23:41.421992image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:23:41.476220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.010732
53.7%
0.09268
46.3%

Most occurring characters

ValueCountFrequency (%)
029268
48.8%
.20000
33.3%
110732
 
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number40000
66.7%
Other Punctuation20000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029268
73.2%
110732
 
26.8%
Other Punctuation
ValueCountFrequency (%)
.20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common60000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029268
48.8%
.20000
33.3%
110732
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII60000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029268
48.8%
.20000
33.3%
110732
 
17.9%

Unit1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing6095
Missing (%)30.5%
Memory size156.4 KiB
0.0
6982 
1.0
6923 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41715
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06982
34.9%
1.06923
34.6%
(Missing)6095
30.5%

Length

2021-11-29T11:23:41.532125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:23:41.584736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.06982
50.2%
1.06923
49.8%

Most occurring characters

ValueCountFrequency (%)
020887
50.1%
.13905
33.3%
16923
 
16.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27810
66.7%
Other Punctuation13905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
020887
75.1%
16923
 
24.9%
Other Punctuation
ValueCountFrequency (%)
.13905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
020887
50.1%
.13905
33.3%
16923
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII41715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
020887
50.1%
.13905
33.3%
16923
 
16.6%

Unit2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing6095
Missing (%)30.5%
Memory size156.4 KiB
1.0
6982 
0.0
6923 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41715
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.06982
34.9%
0.06923
34.6%
(Missing)6095
30.5%

Length

2021-11-29T11:23:41.640111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:23:41.692913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.06982
50.2%
0.06923
49.8%

Most occurring characters

ValueCountFrequency (%)
020828
49.9%
.13905
33.3%
16982
 
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27810
66.7%
Other Punctuation13905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
020828
74.9%
16982
 
25.1%
Other Punctuation
ValueCountFrequency (%)
.13905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
020828
49.9%
.13905
33.3%
16982
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII41715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
020828
49.9%
.13905
33.3%
16982
 
16.7%

HospAdmTime
Real number (ℝ)

ZEROS

Distinct8890
Distinct (%)44.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-55.072586
Minimum-5366.86
Maximum0
Zeros1145
Zeros (%)5.7%
Negative18855
Negative (%)94.3%
Memory size156.4 KiB
2021-11-29T11:23:41.754939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-5366.86
5-th percentile-244.829
Q1-52.3525
median-8.57
Q3-3.38
95-th percentile0
Maximum0
Range5366.86
Interquartile range (IQR)48.9725

Descriptive statistics

Standard deviation135.5956936
Coefficient of variation (CV)-2.462126867
Kurtosis241.4449376
Mean-55.072586
Median Absolute Deviation (MAD)8.52
Skewness-10.8324003
Sum-1101451.72
Variance18386.19213
MonotonicityNot monotonic
2021-11-29T11:23:41.861404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01145
 
5.7%
-0.02238
 
1.2%
-0.01170
 
0.9%
-0.03134
 
0.7%
-0.04125
 
0.6%
-0.05104
 
0.5%
-0.0794
 
0.5%
-0.0678
 
0.4%
-0.0970
 
0.4%
-0.0855
 
0.3%
Other values (8880)17787
88.9%
ValueCountFrequency (%)
-5366.861
< 0.1%
-3397.641
< 0.1%
-3342.341
< 0.1%
-3189.391
< 0.1%
-3112.121
< 0.1%
-2929.371
< 0.1%
-2842.111
< 0.1%
-2667.341
< 0.1%
-2384.781
< 0.1%
-2382.341
< 0.1%
ValueCountFrequency (%)
01145
5.7%
-0.016
 
< 0.1%
-0.01170
 
0.9%
-0.013
 
< 0.1%
-0.026
 
< 0.1%
-0.02238
 
1.2%
-0.026
 
< 0.1%
-0.0337
 
0.2%
-0.03134
 
0.7%
-0.0326
 
0.1%

ICULOS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct229
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.683325
Minimum4.5
Maximum170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:42.042790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile7.5
Q112
median20
Q324
95-th percentile29.5
Maximum170
Range165.5
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.64465905
Coefficient of variation (CV)0.5916002021
Kurtosis44.00773376
Mean19.683325
Median Absolute Deviation (MAD)6
Skewness4.964837969
Sum393666.5
Variance135.5980843
MonotonicityNot monotonic
2021-11-29T11:23:42.142614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20682
 
3.4%
19.5663
 
3.3%
20.5652
 
3.3%
18.5643
 
3.2%
21643
 
3.2%
22608
 
3.0%
21.5594
 
3.0%
19575
 
2.9%
22.5570
 
2.9%
11558
 
2.8%
Other values (219)13812
69.1%
ValueCountFrequency (%)
4.5193
1.0%
5102
 
0.5%
5.5115
 
0.6%
6105
 
0.5%
6.5145
 
0.7%
7176
0.9%
7.5202
1.0%
8294
1.5%
8.5325
1.6%
9375
1.9%
ValueCountFrequency (%)
1701
 
< 0.1%
1691
 
< 0.1%
168.55
< 0.1%
1681
 
< 0.1%
167.51
 
< 0.1%
1641
 
< 0.1%
163.51
 
< 0.1%
160.51
 
< 0.1%
159.51
 
< 0.1%
156.52
 
< 0.1%

SepsisLabel
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct295
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02389112032
Minimum0
Maximum1
Zeros18858
Zeros (%)94.3%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:42.251076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.07438016529
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1289478231
Coefficient of variation (CV)5.397311693
Kurtosis42.14090161
Mean0.02389112032
Median Absolute Deviation (MAD)0
Skewness6.400592311
Sum477.8224064
Variance0.01662754107
MonotonicityNot monotonic
2021-11-29T11:23:42.355791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
018858
94.3%
1223
 
1.1%
0.909090909126
 
0.1%
0.833333333319
 
0.1%
0.666666666719
 
0.1%
0.454545454516
 
0.1%
0.769230769214
 
0.1%
0.476190476214
 
0.1%
0.227272727313
 
0.1%
0.62513
 
0.1%
Other values (285)785
 
3.9%
ValueCountFrequency (%)
018858
94.3%
0.0059880239521
 
< 0.1%
0.0063291139241
 
< 0.1%
0.011627906981
 
< 0.1%
0.013793103451
 
< 0.1%
0.017857142861
 
< 0.1%
0.02214022141
 
< 0.1%
0.022222222221
 
< 0.1%
0.026865671641
 
< 0.1%
0.028846153851
 
< 0.1%
ValueCountFrequency (%)
1223
1.1%
0.909090909126
 
0.1%
0.93
 
< 0.1%
0.833333333319
 
0.1%
0.81818181822
 
< 0.1%
0.769230769214
 
0.1%
0.751
 
< 0.1%
0.714285714313
 
0.1%
0.69230769233
 
< 0.1%
0.666666666719
 
0.1%

Sepsis
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
0.0
18858 
1.0
 
1142

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.018858
94.3%
1.01142
 
5.7%

Length

2021-11-29T11:23:42.452345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:23:42.506441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.018858
94.3%
1.01142
 
5.7%

Most occurring characters

ValueCountFrequency (%)
038858
64.8%
.20000
33.3%
11142
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number40000
66.7%
Other Punctuation20000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
038858
97.1%
11142
 
2.9%
Other Punctuation
ValueCountFrequency (%)
.20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common60000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
038858
64.8%
.20000
33.3%
11142
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII60000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
038858
64.8%
.20000
33.3%
11142
 
1.9%

Hours
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct230
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.09975
Minimum8
Maximum336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:42.571665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14
Q123
median38
Q347
95-th percentile58
Maximum336
Range328
Interquartile range (IQR)24

Descriptive statistics

Standard deviation23.27525267
Coefficient of variation (CV)0.6109030287
Kurtosis44.03440685
Mean38.09975
Median Absolute Deviation (MAD)12
Skewness4.965291886
Sum761995
Variance541.7373868
MonotonicityNot monotonic
2021-11-29T11:23:42.672926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39697
 
3.5%
38667
 
3.3%
36664
 
3.3%
40651
 
3.3%
41639
 
3.2%
43610
 
3.0%
42596
 
3.0%
37591
 
3.0%
21564
 
2.8%
44563
 
2.8%
Other values (220)13758
68.8%
ValueCountFrequency (%)
8204
1.0%
9114
 
0.6%
10126
 
0.6%
11120
 
0.6%
12164
0.8%
13205
1.0%
14218
1.1%
15305
1.5%
16327
1.6%
17373
1.9%
ValueCountFrequency (%)
3365
< 0.1%
3352
 
< 0.1%
3341
 
< 0.1%
3331
 
< 0.1%
3271
 
< 0.1%
3261
 
< 0.1%
3201
 
< 0.1%
3181
 
< 0.1%
3122
 
< 0.1%
3102
 
< 0.1%

Interactions

2021-11-29T11:23:30.601296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:23.522965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:23.796757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:23.977800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:24.158952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:24.329755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:24.511992image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:24.685088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:24.857809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:25.028592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:25.194024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:25.363286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:25.517705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:25.688195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:25.930177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:26.096317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:26.266593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:26.434994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:26.619213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:26.800810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:26.971356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:27.140906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:27.312528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:27.485950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:27.672920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:27.915087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:28.093910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:28.266448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:28.448307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:28.622443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:28.792125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:28.963587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:29.129655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:29.296836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:29.472546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:29.646860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:29.818681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:30.062537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:30.243389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:30.424817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:30.690897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:23.621792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:23.885153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:24.066630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:24.243107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:24.418998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:24.596590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:24.769737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:24.942502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:25.109535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:25.276857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:25.438845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:25.601120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:25.845210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:26.011322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:26.179481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:26.348368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:26.526285image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:26.707641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:26.885310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:27.053592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:27.226092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:27.396500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:27.578808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:27.753553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:28.002510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:28.178207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:28.356343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:28.533734image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:28.704884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:28.876099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:29.045735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:29.210913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:29.383539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:29.557111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:29.730856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:29.977024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:30.150487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:30.332551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:30.511068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-29T11:23:42.819931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-29T11:23:43.163962image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-29T11:23:43.508584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-29T11:23:43.794036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-11-29T11:23:30.934909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-29T11:23:32.026338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-11-29T11:23:32.743322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-11-29T11:23:33.554149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

PatientIDHRO2SatTempSBPMAPDBPRespEtCO2BaseExcessHCO3FiO2pHPaCO2SaO2ASTBUNAlkalinephosCalciumChlorideCreatinineBilirubin_directGlucoseLactateMagnesiumPhosphatePotassiumBilirubin_totalTroponinIHctHgbPTTWBCFibrinogenPlateletsAgeGenderUnit1Unit2HospAdmTimeICULOSSepsisLabelSepsisHours
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Last rows

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